A Project on

LATENT FINGERPRINT MATCHING USING AUTOMATED

FINGERPRINT IDENTIFICATION SYSTEM

Submitted in partial fulfillment of the requirements for the degree of

Bachelor of Technology

in

Electronics and Communication Engineering

by

Manish Negi

Pratiksha Yadav

Shubham

Rishi Raj Singh Rawat

Under the guidance of

Mr. Manoj Kumar

DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING

G. B. PANT ENGINEERING COLLEGE, PAURI, UTTARAKHAND, INDIA

JUNE 2015

DECLARATION

We hereby declare that this dissertation entitled “LATENT FINGERPRINT MATCH-

ING USING AUTOMATED FINGERPRINT IDENTIFICATION SYSTEM” submitted

to the Department of Electronics and Communication Engineering, G. B. Pant Engi-

neering College, Pauri Garhwal (Uttarakhand) for the award of Bachelor of Technology

degree in Electronics and Communication Engineering is a bonafide work carried out

by us under the guidance of Mr. Manoj Kumar and that it has not been submitted

anywhere for any award. Where other sources of information have been used, they have

been acknowledged.

Date: 08 June 2015 Manish Negi

Place: GBPEC, Pauri Pratiksha Yadav

Shubham

Rishi Raj Singh Rawat

CERTIFICATE

This is to certify that the dissertation entitled “LATENT FINGERPRINT MATCHING

USING AUTOMATED FINGERPRINT IDENTIFICATION SYSTEM” being submit-

ted by Manish Negi, Pratiksha Yadav, Shubham and Rishi Raj Singh Rawat in the

partial fulfilment of the requirements for the award of Bachelor of Technology degree

in Electronics and Communication Engineering to the Department of Electronics and

Communication Engineering, G. B. Pant Engineering College, Pauri Garhwal (Uttarak-

hand) is a bonafide work carried out by them under my guidance and supervision.

To the best of my knowledge, the matter embodied in the dissertation has not been

submitted for the award of any other degree or diploma.

Date: 08 June 2015 ˜ Mr. Manoj Kumar

Place: GBPEC, Pauri Assistant Professor

˜ SUPERVISOR

PREFACE

Among all the biometric techniques, fingerprint-based identification is the oldest method

which has been successfully used in numerous applications. Everyone has unique, im-

mutable fingerprints. Identifying suspects based on impressions of fingers lifted from

crime scenes (latent prints) is a routine procedure that is extremely important to foren-

sics and law enforcement agencies. Latents are partial fingerprints that are usually

smudgy, with small area and containing large distortion. Due to these characteristics,

latents have a significantly smaller number of minutiae points compared to full (rolled

or plain) fingerprints.

A fingerprint is made of a series of ridges and furrows on the surface of the finger. The

uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as

well as the minutiae points. Minutiae points are local ridge characteristics that occur

at either a ridge bifurcation or a ridge ending. Minutiae are very important features for

fingerprint representation, and most practical fingerprint recognition systems store only

the minutiae template in the database for further usage.

ACKNOWLEDGEMENT

We place on record and warmly acknowledge the continuous encouragement, invaluable

supervision, timely suggestions and inspired guidance offered by our guide Mr. Manoj

Kumar, Assistant Professor,Department of Electronics & Communication Engineering,

G. B. Pant Engineering College, Pauri Garhwal (Uttarakhand) in bringing this project

to a successful completion. We are also grateful to Dr. Y. Singh, Head and Professor,

Electronics & Communication Engineering Department and Dr. A. K. Gautam, As-

sociate Professor, Electronics & Communication Engineering Department, G. B. Pant

Engineering College, Pauri Garhwal (Uttarakhand) for helping us through the entire

duration of the project. Last but not the least we express our sincere thanks to all our

friends who have patiently extended all kind of help for accomplishing this undertaking.

Our sincere thanks and acknowledgements are due to all our family members who have

constantly encouraged us for completing this project.

Manish Negi

Pratiksha Yadav

Shubham

Rishi Raj Singh Rawat

ABSTRACT

In this project, we propose a new fingerprint matching algorithm which is especially

designed for matching latents. The proposed algorithm uses a robust alignment algo-

rithm (local based descriptor MCC) to align fingerprints and measure similarities be-

tween fingerprints by considering both minutiae and orientation field information. The

conventional methods that utilize minutiae information treat them as a point set and

find the matched points from different minutiae sets. These minutiae are used for fin-

gerprint recognition, in which the fingerprint’s orientation field is reconstructed from

virtual minutiae and further utilized in the matching stage to enhance the system’s per-

formance. A decision fusion scheme is used to combine the reconstructed orientation

field matching with conventional minutiae based matching. Since orientation field is an

important global feature of fingerprints, the proposed method can obtain better results

than conventional methods. In our project it is implemented using MATLAB-GUI where

virtual minutiae are considered.

CONTENTS

Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Certificate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4 Fingerprint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.5 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.5.1 Minutiae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.5.2 Orientation field . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.6 Need For Automated Extraction System . . . . . . . . . . . . . . . 6

1.7 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2. FINGERPRINT ENHANCEMENT TECHNIQUE . . . . . . . . . . . 9

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Binarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2.1 Thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3 Thinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3. FEATURE EXTRACTION . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4 Orientation Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.5 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4. DATABASE AND FINGERPRINT MATCHING . . . . . . . . . . . . 21

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2 Database FVC2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.3 Fingerprint Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.3.1 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.3.2 Similarity Measure . . . . . . . . . . . . . . . . . . . . . . . . . . 25

5. IMPLEMENTATION OF THE PROPOSED ALGORITHM . . . . . 26

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.2 Enhancement of the fingerprint image . . . . . . . . . . . . . . . . 26

5.3 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.3.1 Ridge Bifurcation . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.3.2 Minutiae Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.3.3 False Minutiae Removal . . . . . . . . . . . . . . . . . . . . . . . 30

5.4 Orientation field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.4.1 Segmentation and Region of interest . . . . . . . . . . . . . . . . 33

5.5 Minutiae Match . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6. RESULT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6.1 Result and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . 35

7. CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

vii

8. APPENDIX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

8.1 Matlab Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

viii

LIST OF FIGURES

1.1 Block Diagram of proposed algorithm. . . . . . . . . . . . . . . . . . . . 2

1.2 Three types of fingerprint impressions. (a) Rolled; (b) plain; (c) latent. . 4

1.3 Ridge Ending and Bifurcation . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 The orientation of a ridge pixel in a fingerprint. . . . . . . . . . . . . . . 6

2.1 Binarized output of a fingerprint . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Thinned output of a fingerprint . . . . . . . . . . . . . . . . . . . . . . . 11

3.1 (a) Mask for bifurcation (b) Mask for termination. . . . . . . . . . . . . . 13

3.2 Examples of a ridge ending and bifurcation pixel. (a) A Crossing Number

of one corresponds to a ridge ending pixel. (b) A Crossing Number of three

corresponds to a bifurcation pixel. . . . . . . . . . . . . . . . . . . . . . . 14

3.3 Minutiae extracted image . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4 (a) Orientation field with white background. (b) Orientation field with

thinned image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.5 Ridges and valleys on a fingerprint image . . . . . . . . . . . . . . . . . . 19

3.6 A fingerprint image and its foreground and background regions . . . . . . 20

4.1 One fingerprint image from each database . . . . . . . . . . . . . . . . . 22

4.2 Sample images from the database. . . . . . . . . . . . . . . . . . . . . . . 23

5.1 (a)Input image.(b) Binarized output . . . . . . . . . . . . . . . . . . . . 26

5.2 (a)Binarized image.(b) Thinned output . . . . . . . . . . . . . . . . . . . 27

5.3 (a)Thinned image. (b) Extracted ridge ending and bifurcation. . . . . . . 29

5.4 (a)Thinned image.(b) Orientation field. . . . . . . . . . . . . . . . . . . . 32

5.5 Marked region of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.6 Similarity Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6.1 Graphical User Interface(GUI) for creating database . . . . . . . . . . . . 35

6.2 Binarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6.3 Thinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6.4 Minutiae extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6.5 Orientation field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6.6 Marked region of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.7 Matching with similar fingerprint . . . . . . . . . . . . . . . . . . . . . . 40

6.8 Matching with non-similar fingerprint . . . . . . . . . . . . . . . . . . . . 40

x

LIST OF TABLES

3.1 Property of crossing number . . . . . . . . . . . . . . . . . . . . . . . . . 13

5.1 Extracted information from image in term of ridge termination and bi-

furcation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6.1 Results after comparing similarities between input with other fingerprints

in database FVC2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.2 Extracted information from image in term of ridge termination ,bifurca-

tion and orientation field . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

CHAPTER 1

INTRODUCTION

1.1 Introduction

Fingerprint recognition has been used by law enforcement agencies to identify suspects

and victims for several decades [1]. Recent advances in automated fingerprint identifi-

cation technology, coupled with the pronounced need for reliable person identification,

have resulted in the increased use of fingerprints in both government and civilian appli-

cations such as border control, employment background check and secure facility access.

Fingerprints obtained during the crime scenes are mostly latent images. Latent Fin-

gerprints refer to the impressions unintentionally left on item handled or touched by

fingers. Such fingerprints are often not directly visible unless some physical or chemical

technique is applied to enhance them. Since the early 20th century latent fingerprints

have served as important evidence for law enforcement agencies to apprehend and con-

vict criminals [2].

Given a latent fingerprint (with manually marked minutiae) and a rolled fingerprint, we

extract additional features from both prints, align them in the same coordinate system,

and compute a match score between them. The proposed matching approach uses minu-

tiae and orientation field from both latent and rolled prints. To enable reliable feature

extraction, a latent fingerprint image, which is often of very poor quality, needs to go

through an image enhancement stage, which connects broken ridges, separates joined

ridges, and removes overlapping patterns. These steps are shown in Fig. 1.1.

Here we consider the problem of biometric verification in a more formal manner. In

a verification problem, the biometric signal from the user is compared against a single

enrolled template. This template is chosen based on the claimed identity of the user.

Each user i is represented by a biometric Bi. It is assumed that there is a one-to-one

correspondence between the biometric Bi and the identity i of the individual. The fea-

ture extraction phase results in a machine representation (template) Ti of the biometric.

During verification; the user claims an identity j and provides a biometric signal Bj.

The feature extractor now derives the corresponding machine [3] representation Tj. The

recognition consists of computing a similarity score S (Ti, Tj). The claimed identity is

assumed to be true if the S(T i, T j) > Th for some threshold Th. The choice of the

threshold also determines the trade-off between user convenience and system security as

will be seen in the ensuing section.

Figure 1.1: Block Diagram of proposed algorithm.

2

1.2 Motivation

The motivation behind this fingerprint image enhancement and minutiae extraction

process is to improve the quality of fingerprint and to extract the minutiae points. And

in the extraction process we should not get the false minutiae and preserve the true ridge

endings and ridge bifurcations. The minutiae extracted from the fingerprint heavily

depends upon the quality of the input fingerprint. In order to extract true minutiae

from the fingerprint we need to remove the noise from the input image and for that we

need an enhancement algorithm.

1.3 Thesis Organization

The dissertation is divided into six chapters and their outline is described as given below:

In chapter 2 we have explained various image enhancement techniques on latent finger-

print for better results during matching process. In chapter 3 we have explained about

the local minutiae descriptor, which is used to extract the information from fingerprint

and also explain about the orientation field detection. In chapter 4 we have explained

about the database used in this project and how matching between two fingerprint is

done.In chapter 5 we have explained about the algorithm implemented to get desired

results. Finally, chapter 6 is dedicated to the result part which shows output of various

operations done.

1.4 Fingerprint

We touch things every day: a coffee cup, a car door, a computer keyboard, etc. Each time

we touch, it is likely that we leave behind our unique signature in our fingerprints. No

two people have exactly the same fingerprints. Even identical twins, with identical DNA,

have different fingerprints. This uniqueness allows fingerprints to be used in all sorts of

ways, including background checks, biometric security, mass disaster identification, and

3

of course, in criminal situations. There are essentially three types of fingerprints in law

enforcement applications:

1. Rolled, which is obtained by rolling the finger nail-to-nail either on a paper (in this

case ink is first applied to the finger surface) or the platen of a scanner as shown

in Fig. 1.2(a).

2. Plain, which is obtained by placing the finger flat on a paper or the platen of a

scanner without rolling as shown in Fig. 1.2(b).

3. Latents, which are lifted from surfaces of objects that are inadvertently touched

or handled by a person typically at crime scenes [3] as shown in Fig. 1.2(c).

(a) (b) (c)

Figure 1.2: Three types of fingerprint impressions. (a) Rolled; (b) plain; (c) latent.

Rolled prints contain the largest amount of information about the ridge structure on

a fingerprint since they capture the largest finger surface area; latent usually contain

the least amount of information for matching or identification because of their size

and inherent noise. Compared to rolled or plain fingerprints, latents are smudgy and

blurred, capture only a small finger area, and have large nonlinear distortion due to

pressure variations.

4

1.5 Feature Extraction

In pattern recognition and in image processing, feature extraction is a special form of

dimensionality education. Transforming the input data into the set of features is called

feature extraction. If the features extracted are carefully chosen it is expected that the

features set will extract the relevant information from the input data in order to perform

the desired task using this reduced representation instead of the full size input [4].

1.5.1 Minutiae

Minutiae refer to the specific plot point on fingerprint. These include characteristics

such as ridge bifurcation and ridge ending as shown in Fig. 1.3.

(a)Ridge Ending- the abrupt end of a ridge

(b)Ridge Bifurcation- a single ridge that divides into two ridges

Figure 1.3: Ridge Ending and Bifurcation

1.5.2 Orientation field

Orientation field defines the local orientation of the ridges contained in the fingerprint

as shown in Fig. 1.4. It is reconstructed from minutiae location and direction for the

latent. It is used to improve fingerprint matching performance

5

Figure 1.4: The orientation of a ridge pixel in a fingerprint.

1.6 Need For Automated Extraction System

1. Reducing the time spent by latent examiners in manual markup. A crime scene

can contain as many as hundreds of latents. However, only a small portion of them

can be processed simply because law enforcement agencies do not have sufficient

manpower. It can take twenty minutes or even longer to mark the minutiae in a

single latent. Automatic feature extraction can improve the efficiency of processing

latents, leading to more identification quickly [5].

2. Improving the compatibility between minutiae in latents and full fingerprints. In

current practice, minutiae in latents are manually marked while minutiae in full

fingerprints are automatically extracted. This can cause a compatibility problem.

Although this compatibility issue is not a severe problem for full fingerprint match-

ing, this problem cannot be underestimated in the case of latent matching, since in

a tiny and smudgy latent, every minutia plays an important role. To address this

issue, AFIS vendors usually provide training courses to latent examiners on how

to better mark minutiae for their particular AFIS system since different vendors

systems are not very consistent in extracting minutiae. However, it takes time for

fingerprint examiners to get familiar with a system. This problem can be alleviated

provided features in latents are also extracted by automatic algorithms [6].

3. Improving repeatability/reproducibility of latent identification. The minutiae in

6

the same latent marked by different latent examiners or even by the same examiner

(but at different times) may not be the same. This is one of reasons why different

latent examiners or even the same examiner (but at different times) make different

matching decisions on the same latent-exemplar pair [7].

1.7 Application

1. Identifying suspects based on impressions of fingers lifted from crime scenes (latent

prints) is a routine procedure that is extremely important to forensics and law

enforcement agencies.

2. Verifying the matching between driver fingerprint and the fingerprint features

stored on the license assures that the driver is indeed the person that the license

is issued for. This task can be done on-site where the fingerprint features obtained

from the driver by live scanning is compared with the features magnetically stored

on the driver license. Current ”smart card” technology allows abundant memory

capacity to store the features on card. A driver/ license match means that the

license indeed belongs to the driver, this, however does no warranty that the driver

license is not falsified. To check for validity of the driver license the police officer

has the option to make additional inquiry against the database. In this case a

license validity check will result.

3. Since 2000, electronic fingerprint readers have been introduced for security ap-

plications such as log-in authentication for the identification of computer users.

However, some less sophisticated devices have been discovered to be vulnerable to

quite simple methods of deception, such as fake fingerprints cast in gels. In 2006,

fingerprint sensors gained popularity in the notebook PC market. Built-in sensors

in Think-Pads, VAIO, HP Pavilion laptops, and others also double as motion de-

tectors for document scrolling, like the scroll wheel. Following the release of the

7

iPhone 5S model, a group of German hackers announced on September 21, 2013,

that they had bypassed Apple’s new Touch ID fingerprint sensor by photographing

a fingerprint from a glass surface and using that captured image as verification.

4. Electronic registration and library access: Fingerprints and, to a lesser extent, iris

scans can be used to validate electronic registration, cashless catering, and library

access. By 2007, this practice was particularly widespread in UK schools, and it

was also starting to be adopted in some states in the US.

8

CHAPTER 2

FINGERPRINT ENHANCEMENT

TECHNIQUE

2.1 Introduction

A critical step in Automatic Fingerprint Matching System is to automatically and re-

liably extract minutiae from input fingerprint images. However the performance of the

minutiae extraction algorithm relies heavily on the quality of the input fingerprint image.

In order to ensure the extraction of true minutiae points, it is essential to incorporate

the enhancement algorithm. Reliable and sound verification of fingerprints in any AFIS

is always preceded with a proper detection and extraction of its features. A fingerprint

image is first enhanced before the features contained in it are detected or extracted.

A well enhanced image will provide a clear separation between the valid and spurious

features. Spurious features are those minutiae points that are created due to noise or

artifacts and they are not actually part of the fingerprint.

2.2 Binarization

Most minutiae extraction algorithms operate on binary images where there are only two

levels of interest: the black pixels that represent ridges, and the white pixels that rep-

resent valleys. Binarization is the process that converts a grey-level image into a binary

image. The binarization process involves examining the grey-level value of each pixel

in the enhanced image as shown in Fig. 2.1 and if the value is greater than the global

threshold, then the pixel value is set to a binary value one; otherwise, it is set to zero.

Equation used to binarize the gray scale images [8].

G(x,y)= 1 if f(x, y) > T

0 if f(x, y) <= T

Where, f(x,y) is the value of a pixel in gray-scale image and g(x,y) is the binarized image

Figure 2.1: Binarized output of a fingerprint

2.2.1 Thresholding

In this method, the grey-level value of each pixel in the filtered image is examined and,

if the value is greater than the threshold value 1, then the pixel value is set to a binary

value one; otherwise, it is set to zero. The threshold value successfully makes each cluster

as tight as possible and also eliminate all the overlaps. The threshold value of 1 is taken

after a careful selection from a series of within and between class variance values ranging

from 0 to 1 that optimally supported the maximum separation of the ridges from the

valleys. The clear separation of the ridges from the valleys verifies the correctness of the

algorithm as proposed in [9] and implemented in this project.

10

2.3 Thinning

The final image enhancement step typically performed prior to minutiae extraction is

thinning. Thinning is a morphological operation that successively erodes away the fore-

ground pixels until they are one pixel wide. A standard thinning algorithm is employed,

which performs the thinning operation using two subiterations. This algorithm is accessi-

ble in MATLAB via the ‘thin’ operation under the bwmorph function. Each subiteration

begins by examining the neighbourhood of each pixel in the binary image, and based on

a particular set of pixel-deletion criteria, it checks whether the pixel can be deleted or

not. These subiterations continue until no more pixels can be deleted. The application

of the thinning algorithm to a fingerprint image preserves the connectivity of the ridge

structures while forming a skeletonized version of the binary image as shown in Fig. 2.2.

This skeleton image is then used in the subsequent extraction of minutiae. The process

Figure 2.2: Thinned output of a fingerprint

involving the extraction of minutiae from a skeleton image will be discussed in the next

chapter.

11

CHAPTER 3

FEATURE EXTRACTION

3.1 Introduction

After a fingerprint image has been enhanced, the next step is to extract the minutiae

from the enhanced image. Following the extraction of minutiae, a final image post

processing stage is performed to eliminate false minutiae. This chapter provides discus-

sion on the methodology and implementation of techniques for minutiae extraction and

orientation field. The proposed matching approach uses minutiae and orientation field

from both latent and rolled prints. Minutiae are manually marked by latent examin-

ers in the latent, and automatically extracted using commercial matchers in the rolled

print. Based on minutiae, local minutiae descriptors are built and used in the proposed

descriptor-based alignment and scoring algorithms. Orientation field is reconstructed

from minutiae location and direction for the latents as proposed in [10],and orientation

field is automatically extracted from the rolled print images by using a gradient-based

method.

3.2 Minutiae Extraction

The most commonly employed method of minutiae extraction is the Crossing Number

(CN) concept [11] [12]. This method involves the use of the skeleton image where the

ridge flow pattern is eight-connected. The minutiae are extracted by scanning the local

neighbourhood of each ridge pixel in the image using a 3×3 window as shown in Fig.

3.1. The CN value is then computed, which is defined as half the sum of the differences

between pairs of adjacent pixels in the eight-neighbourhood. Using the properties of the

CN as shown in Table 3.1, the ridge pixel can then be classified as a ridge ending, bifur-

cation or non-minutiae point. For example, a ridge pixel with a CN of one corresponds

to a ridge ending, and a CN of three corresponds to a bifurcation.

(a) (b)

Figure 3.1: (a) Mask for bifurcation (b) Mask for termination.

Table 3.1: Property of crossing number

CN Property0 Isolated point1 Ridge ending point2 Continuing ridge ending3 Birufication ending4 Crossing point

This approach involves using a 3×3 window to examine the local neighbourhood of

each ridge pixel in the image. A pixel is then classified as a ridge ending if it has only

one neighbouring ridge pixel in the window, and classified as a bifurcation if it has three

neighbouring ridge pixels. Consequently, it can be seen that this approach is very similar

to the Crossing Number method. The CN for a ridge pixel P is given by eq. 3.1 [13].

13

CN = 0.5

8∑

i=1

|Pi − Pi+1|, P9 = P1 (3.1)

where Pi is the pixel value in the neighbourhood of P. For a pixel P, its eight neighbouring

pixels are scanned in an anti-clockwise direction.

After the CN for a ridge pixel has been computed, the pixel can then be classified

according to the property of its CN value. As shown in Figure 3.2, a ridge pixel with a

CN of one corresponds to a ridge ending, and a CN of three corresponds to a bifurcation

as shown in Fig. 3.2. For each extracted minutiae point, the following information is

recorded:

1. x and y coordinates,

2. orientation of the associated ridge segment, and

3. type of minutiae (ridge ending or bifurcation).

(a)CN=1 (b)CN=3

Figure 3.2: Examples of a ridge ending and bifurcation pixel. (a) A Crossing Numberof one corresponds to a ridge ending pixel. (b) A Crossing Number ofthree corresponds to a bifurcation pixel.

We propose the use of local minutiae descriptor known as Minutiae Cylindrical Code

(MCC) to improve the robustness against distortion. Local Minutiae Descriptor: Local

descriptors have been widely used in fingerprint matching (e.g. [14, 15]). Feng and

Zhou [16] evaluated the performance of local descriptors associated with fingerprint

matching in four categories of fingerprints: good quality, poor quality, small common

region, and large plastic distortion. They also coarsely classified the local descriptors as

14

image-based, texture-based, and minutiae-based descriptors. Minutiae cylinder records

the neighborhood information of a minutiae as a 3-D function and minutiae extracted

image is shown in Fig. 3.3. The cylinder contains several layers and each layer represents

the density of neighboring minutiae along the corresponding direction. The cylinder can

be concatenated as a vector, and therefore the similarity between two minutiae cylinders

can be efficiently computed.

Figure 3.3: Minutiae extracted image

3.3 Normalization

The next step in the fingerprint enhancement process is image normalization. Normal-

ization is used to standardize the intensity values in an image by adjusting the range of

grey-level values so that it lies within a desired range of values. Let I(i,j) represent the

grey-level value at pixel (i,j) and N represent the normalized grey-level value at pixel

(i,j). The equation for normalized image is defined in eq. 3.2:

N(i, j) =

M0 +√

V0(I(i,j)−M)2

V, if I(i, j) > M

M0 −√

V0(I(i,j)−M)2

V, otherwise

(3.2)

15

Where M and V are the estimated mean and variance of I(i, j), respectively, and M0

and V0 are the desired mean and variance values, respectively. Normalization does not

change the ridge structures in a fingerprint; it is performed to standardize the dynamic

levels of variation in grey-level values, which facilitates the processing of subsequent

image enhancement stage.

3.4 Orientation Field

Orientation field can be used in several ways to improve fingerprint matching perfor-

mance, such as by matching orientation fields directly and fusing scores with other

matching scores, or by enhancing the images to extract more reliable features. Orienta-

tion field estimation using gradient-based method is very reliable [13] in good quality im-

ages. However, when the image contains noise, this estimation becomes very challenging.

A few model-based orientation field estimation methods have been proposed [17,18]that

use singular points as input to the model. In the latent fingerprint matching case, it is

very challenging to estimate the orientation field based only on the image due to the

poor quality and small area of the latent. Moreover, if singular points are to be used,

they need to be manually marked (and they are not always present) in the latent finger-

print image. Hence, we use a minutiae-based orientation field reconstruction algorithm

proposed, which takes manually marked minutiae in latents as input and outputs an ori-

entation field as shown in Fig. 3.4. This approach estimates the local ridge orientation

in a block by averaging the direction of neighboring minutiae. The orientation field is

reconstructed only inside the convex hull of minutiae. Since the directions of manually

marked minutiae are very reliable, the orientation field reconstructed using this approach

is quite accurate except in areas absent of minutiae or very close to singular points. For

rolled fingerprints, orientation field is automatically extracted using a gradient- based

method.

The steps for calculating the orientation at pixel are as follows:

16

(a) (b)

Figure 3.4: (a) Orientation field with white background. (b) Orientation field withthinned image.

1. Firstly, a block of size W x W is centered at pixel (i,j) in the normalized fingerprint

image.

2. For each pixel in the block, compute the gradients x(i,j) and y(i,j) which are the

gradient magnitudes in the x and y directions, respectively. The horizontal Sobel

operator is used to compute x(i,j) :

1 0 −1

2 0 −2

1 0 −1

The vertical Sobel operator is used to compute y(i,j):

1 2 1

0 0 0

−1 −2 −1

3. The local orientation at pixel (i,j) can then be estimated using the following eq.3.3,

3.4 and 3.5.

17

vx(i, j) =

i+w

2∑

u=i−w

2

j+w

2∑

v=j−w

2

2∂x(u, v)∂y(u, v), (3.3)

vy(i, j) =

i+w

2∑

u=i−w

2

j+w

2∑

v=j−w

2

(∂2x(u, v)∂

2y(u, v)), (3.4)

θ(i, j) =1

2tan−1

[

vx(i, j)

vy(i, j)

]

, (3.5)

Where (i,j) is the least square estimate of the local orientation at the block centered

at pixel(i,j).

4. Smooth the orientation field in a local neighborhood using a Gaussian filter. The

orientation image is firstly converted into a continuous vector field, which is de-

fined by eq. 3.6 and 3.7.

φx(i, j) = cos(2θ(i, j)), (3.6)

φy(i, j) = sin(2θ(i, j)), (3.7)

Where x and y are the x and y components of the vector field, respectively. After

the vector field has been computed, Gaussian smoothing is then performed and

given by eq. 3.8 and 3.9.

φx(i, j) =

w

2∑

u=−w

2

w

2∑

v=−w

2

G(u, v)φx(i− uw, j − vx), (3.8)

φy(i, j) =

w

2∑

u=−w

2

w

2∑

v=−w

2

G(u, v)φy(i− uw, j − vx), (3.9)

Where, G is a Gaussian low-pass filter of size w x w.

18

5. The final smoothed orientation field O at pixel(i,j) is defined by eq. 3.10.

O(i, j) =1

2tan−1

[

φx(i, j)

φy(i, j)

]

, (3.10)

3.5 Segmentation

There are two regions that describe any fingerprint image; namely the foreground region

and the background region. The foreground regions are the regions containing the ridges

and valleys. As shown in Fig. 3.5, the ridges are the raised and dark regions of a

fingerprint image while the valleys are the low and white regions between the ridges.

The foreground regions, often referred to as the Region of Interest (RoI) is shown in Fig.

3.6. The background regions are mostly the outside regions where the noises introduced

into the image during enrolment are mostly found. The essence of segmentation is to

reduce the burden associated with image enhancement by ensuring that focus is only on

the foreground regions while the background regions are ignored.

Figure 3.5: Ridges and valleys on a fingerprint image

The background regions possess very low grey-level variance values while the fore-

ground regions possess very high grey-level variance values. A block processing approach

used in [19] [20] is adopted in this research for obtaining the grey-level variance values.

19

The approach firstly divides the image into blocks of size W x W and then the variance

V(k) for each of the pixels in block k is obtained from eq. 3.11 and 3.12.

V (k) =1

W 2

W∑

i=1

W∑

i=1

(I(i, j)−M(k))2 (3.11)

M(k) =1

W 2

W∑

a=1

W∑

b=1

J(a, b) (3.12)

I(i,j) and J(a,b) are the grey-level value for pixel (i,j) and (a,b) respectively in block k.

Figure 3.6: A fingerprint image and its foreground and background regions

20

CHAPTER 4

DATABASE AND FINGERPRINT

MATCHING

4.1 Introduction

In this chapter we report the orientation field estimation performance and the resulting

matching performances on the FVC2002 latent fingerprint database and an overlapped

fingerprint input. Finally, we discuss the impact of reference fingerprints on orientation

field estimation.

4.2 Database FVC2002

FVC2002 is the Second International Competition for Fingerprint Verification Algo-

rithms. The evaluation was held in April 2002 and the results of the 31 participants

were presented at 16th ICPR (International Conference on Pattern Recognition). This

initiative is organized by D. Maio, D. Maltoni, R. Cappelli from Biometric Systems Lab

(University of Bologna), J. L. Wayman from the U.S. National Biometric Test Center

(San Jose State University) and A. K. Jain from the Pattern Recognition and Image Pro-

cessing Laboratory of Michigan State University. A sample image from each database

FVC2002 is shown in Fig. 4.1.

The size of database FVC2002 is established as 110 fingers, 8 impressions per finger

(880 impressions) (Fig. 4.2). Collecting some additional data provides a margin in

case of collection errors, and also allowed us to systematically choose from the collected

Figure 4.1: One fingerprint image from each database

impressions to include in the test databases. An automatic all-against-all comparison

was first performed by using an internally-developed fingerprint matching algorithm, to

discover possible data-collection errors. False match and false non-match errors were

manually analyzed: two labeling errors were discovered and removed. Fingerprints in

each database were then sorted by quality according to a quality index [21]. The top-

ten quality fingers were removed from each database since they do not constitute an

interesting case study. The remaining 110 fingers were split into set A (100 fingers -

evaluation set) and set B (10 fingers - training set). To make set B representative of

the whole database, the 110 collected fingers were ordered by quality, then the 8 images

from every tenth finger were included in set B. The remaining fingers constituted set A.

After training, set B were made available to the participants, some of them informed

us of the presence of fingerprint pairs whose relative rotation exceeded the maximum

specification of about 35 degrees. We were not much surprised by this, since although

the persons in charge of data collection were informed of the constraint, the require-

22

ment of exaggerating rotation but remaining within a maximum of about 35 degrees

between any two samples is not simple to implement in practice, especially when the

volunteers are untrained users. A further semiautomatic analysis was then necessary

to ensure that, in the evaluation set A, the samples were compliant with the initial

specifications: maximum rotation and non-null overlap between any two impressions of

the same finger. Software was developed to support us in this daunting task. All of

the 12 originally collected impressions of the same fingers were displayed at the same

time and the authors selected a subset of 8 impressions by point and click. Once the

selection was made, the software automatically compared the selected impressions and

warning was issued in case the rotation or displacement between any two pairs exceeded

the maximum allowed. Fortunately, the 12 samples at our disposal always allowed us to

find a subset of 8 impressions compliant with the specification.

Figure 4.2: Sample images from the database.

4.3 Fingerprint Matching

In order to estimate the alignment error, we use ground truth mated minutiae pairs from

FVC2002, which are marked by fingerprint examiners, to compute the average distance

between the true mated pairs after alignment. If the average Euclidean distance for a

given latent is less than a prespecified number of pixels in at least one of the ten best

alignments then we consider it a correct alignment. This alignment is done for removal

of false minutiae detected in latent sample.

23

4.3.1 Alignment

In the latent matching case, singularities are not always present in latents, making it

difficult to base the alignment of the fingerprint on singular points alone. To obtain

manually marked orientation field is expensive, and to automatically extract orientation

field from a latent image is a very challenging problem. Since manually marking minutiae

is a common practice for latent matching, our approach to align two fingerprints is based

on minutiae. Local descriptors can also be used to align two fingerprints. In this case,

usually the most similar minutiae pair is used as a base for the transformation parameters

(rotation and translation), and the most similar pair is chosen based on a measure of

similarity between the local descriptors of the minutiae pair Given two sets of points

(minutiae), a matching score is computed for each transformation in the discretized

set of all allowed transformations. For each pair of minutiae, one minutia from each

image (latent or full), and for given scale and rotation parameters, unique translation

parameters can be computed. Each parameter receives a vote that is proportional to the

matching score for the corresponding transformation. In our approach, the alignment is

conducted in a similar way, but the evidence for each parameter is accumulated based

on the similarity between the local descriptors of the two involved minutiae, with the

similarity and descriptor. The assumption here is that true mated minutiae pairs will

vote for very similar sets of alignment parameters, while non-mated minutiae pairs will

vote randomly throughout the parameter space. As a result, the set of parameters

that presents the highest evidence is considered the best one. For robustness, ten sets

of alignment parameters with strong evidence are considered. In order to make the

alignment computationally efficient and also more accurate, we use the minutiae pairs

that vote for a peak to compute a rigid transformation between the two fingerprints.

The use of voting minutiae pairs to compute the transformation gives more accurate

alignment parameters than directly using the peak parameters.

24

4.3.2 Similarity Measure

For each of the 10 different alignments, a matching score between two fingerprints is

computed by comparing minutiae and orientation fields. The maximum value of the 10

scores is chosen as the final matching score between the two fingerprints. To compute

minutiae matching score under a given alignment, we first find the corresponding minu-

tiae pairs (one in the latent, one in the rolled print). For this purpose, we align the

minutiae sets of the two fingerprints and then find a one-to-one matching between the

two minutiae sets using a greedy algorithm is expressed by eq. 4.1. For each minutia

ml in the latent, a set of candidate minutiae in the rolled print is found. A minutia mr

in the rolled print is called a candidate if it has not been yet matched to any minutia,

and both its location and angle are sufficiently close to ml. The threshold values Ts

for spatial distance and TA for angle distance were determined empirically. Among all

candidates, the one closest to ml in location is chosen as the matching minutia of ml.

SM =1

N

N∑

i=1

(sc(i))(ss(i)) (4.1)

where sc(i)denotes the similarity between the minutiae cylinder codes of the ithpair of

matched minutiae ss(i)=1-(ds(i)/2T(s)) maps the spatial distance ds(i)of the ithpair of

matched minutiae into a similarity score, and denotes the number of minutiae in the

latent. According to equation, the matching score depends on the number of matching

minutiae, which itself is affected by the distance threshold. However, due to large

distortion present in many latents, it is difficult to choose an appropriate value for.

While a large threshold value will lead to more matching minutiae for distorted mated

pairs, the number of matching minutiae for non-mated pairs will increase too. Hence,

we use two different values (15 pixels and 25 pixels) and for each threshold, a set of

matching minutiae is found and a matching score is computed using the above equation.

25

CHAPTER 5

IMPLEMENTATION OF THE

PROPOSED ALGORITHM

5.1 Introduction

Using MATLAB Version 7.11.0 (R2010b), both proposed enrolment and verification

phases are implemented as described in next three subsections.

5.2 Enhancement of the fingerprint image

(a) (b)

Figure 5.1: (a)Input image.(b) Binarized output

The first step is to enhance the fingerprint image by setting the contrast level using

imadjust() function. Then binarization of the image is done above threshold value of

160. Binarization results on a monochrome image is shown in Fig. 5.1.

Then thinning is done by using function bwmorph() function as shown in Fig. 5.2, which

is a morphological operator which operate on binary images and applies the operation

n times and can be Inf, in which case the operator is repeated until the image no longer

changes.

Syntax:

BW= bwmorph(bwoperation)

When used with the thin option, bwmorph() uses the following algorithm:

1. Divide the image into two distinct subfields checker board pattern.

2. In the first subiteration, delete pixel p from the first subfield.

3. In the second subiteration, delete pixel p from second subfield.

(a) (b)

Figure 5.2: (a)Binarized image.(b) Thinned output

The two subiterations together make up one iteration of the thinning algorithm.

When the user specifies an infinite number of iterations (n=Inf), the iterations are

repeated until the image stops changing. The conditions are all tested using applylut

with precomputed lookup tables.

27

5.3 Minutiae Extraction

Ridge Ending

Ridge endings are found by using nlfilter() function as shown in Fig. ??. It consists of

general sliding-neighborhood operations.

Syntax:

B = nlfilter(A, [m n], fun)

B = nlfilter(A, ’indexed’,...)

B = nlfilter(A, [m n], fun) applies the function fun to each m-by-n sliding block of the

grayscale image A. fun is a function that accepts an m-by-n matrix as input and returns

a scalar result.

c = fun(x), fun must be a function handle.

Parameterizing Functions, in the MATLAB Mathematics documentation, explains how

to provide additional parameters to the function fun, c is the output value for the center

pixel in the m-by-n block x. nlfilter calls fun for each pixel in A. nlfilter zero-pads the

m-by-n block at the edges, if necessary.

B = nlfilter(A, ’indexed’,...) processes A as an indexed image, padding with 1’s if A is

of class single or double and 0’s if A is of class logical, uint8, or uint16.

5.3.1 Ridge Bifurcation

Ridge bifurcations are found by using bwlabel() function as shown in Fig. ??. It uses

label connected components in 2-D binary image.

Syntax:

L = bwlabel(BW, n)

[L, num] = bwlabel(BW, n)

L = bwlabel(BW, n) returns a matrix L, of the same size as BW, containing labels for

the connected objects in BW. The variable n can have a value of either 4 or 8, where

28

4 specifies 4-connected objects and 8 specifies 8-connected objects. If the argument is

omitted, it defaults to 8.

The elements of L are integer values greater than or equal to 0. The pixels labeled 0 are

the background. The pixels labeled 1 make up one object; the pixels labeled 2 make up

a second object; and so on.

[L, num] = bwlabel(BW, n) returns in num the number of connected objects found in

BW.

(a) (b)

Figure 5.3: (a)Thinned image. (b) Extracted ridge ending and bifurcation.

5.3.2 Minutiae Table

For constructing minutiae table 5.1, we used round towards infinity function seil(). It

rounds the elements of A to the nearest integers greater than or equal to A. For complex

A, the imaginary and real parts are rounded independently.

Syntax:

B=seil(A)

29

Table 5.1: Extracted information from image in term of ridge termination and bifur-cation.

Ridge termination Ridge bifurcation144 60150 66172 97127 109146 120212 127191 131168 136153 145132 152115 157211 157215 162133 192114 197180 202139 208211 214192 215145 218167 225163 232168 239190 241215 243194 246158 249

5.3.3 False Minutiae Removal

The preprocessing stage does not totally heal the fingerprint image. For example, false

ridge breaks due to insufficient amount of ink and ridge cross-connections due to over

inking are not totally eliminated. Actually all the earlier stages themselves occasionally

introduce some artifacts which later lead to spurious minutiae. These false minutiae

will significantly affect the accuracy of matching if they are simply regarded as genuine

30

minutiae.

Our procedures in removing false minutiae are

1. If the distance between one bifurcation and one termination is less than D and

the two minutiae are in the same ridge, remove both of them. Where D is the

average inter-ridge width representing the average distance between two parallel

neighbouring ridges.

2. If the distance between two bifurcations is less than D and they are in the same

ridge, remove the two bifurcations.

3. If two terminations are within a distance D and their directions are coincident with

a small angle variation and they suffice the condition that no other termination is

located between the two terminations, then the two terminations are regarded as

false minutia derived from a broken ridge and are removed.

4. If two terminations are located in a short ridge with length less than D, remove

the two terminations.

5.4 Orientation field

1. fspecial( ) function creates predefined 2-D filter.

Syntax:

h = fspecial(type)

h = fspecial(type, parameters)

h = fspecial(type) creates a two-dimensional filter h of the specified type. fspecial

returns h as a correlation kernel, which is the appropriate form to use with imfilter.

type is a string having one of these values.

For e.g., h = fspecial(’gaussian’, hsize, sigma) returns a rotationally symmetric

Gaussian lowpass filter of size hsize with standard deviation sigma (positive). hsize

31

can be a vector specifying the number of rows.

2. filter2() function works as a 2-D digital filter.

Syntax:

Y = filter2(h,X)

Y = filter2(h,X,shape)

Y = filter2(h,X) filters the data in X with the two-dimensional FIR filter in the

matrix h. It computes the result, Y, using two-dimensional correlation, and returns

the central part of the correlation that is the same size as X.

(a) (b)

Figure 5.4: (a)Thinned image.(b) Orientation field.

3. quiver() function gives quiver or velocity plot as shown in Fig. 5.4.

Syntax:

a=quiver(x,y)

A quiver plot displays velocity vectors as arrows with components (u,v) at the

points (x,y).

32

5.4.1 Segmentation and Region of interest

Another function regionprops() is used which measures properties of image regions which

is used to find region of interest as shown in Fig. 5.5.

Syntax:

STATS = regionprops(L, properties)

STATS = regionprops(L, properties) measures a set of properties for each labeled region

in the label matrix L. Positive integer elements of L correspond to different regions. For

example, the set of elements of L equal to 1 corresponds to region 1; the set of elements

of L equal to 2 corresponds to region 2; and so on.

STATS is a structure array with length equal to the number of objects in BW, CC.NumObjects,

or max(L(:)). The fields of the structure array denote different properties for each re-

gion, as specified by properties.

Figure 5.5: Marked region of interest

33

5.5 Minutiae Match

Given two set of minutiae of two fingerprint images, the minutiae match algorithm

determines whether the two minutiae sets are from the same finger or not. Fig. 5.6

shows similarity measure between two fingerprints.

Figure 5.6: Similarity Comparison

An alignment-based match algorithm includes two consecutive stages: one is alignment

stage and the second is match stage.

1. Alignment stage: Given two fingerprint images to be matched, choose any minutiae

from each image; calculate the similarity of the two ridges associated with the two

referenced minutiae points. If the similarity is larger than a threshold, transform

each set of minutiae to a new coordination system whose origin is at the referenced

point and whose x-axis is coincident with the direction of the referenced point.

2. Match stage: After we get two set of transformed minutiae points, we use the

elastic match algorithm to count the matched minutiae pairs by assuming two

minutiae having nearly the same position and direction are identical.

34

CHAPTER 6

RESULT

6.1 Result and Discussions

This chapter consists of the results generated by the GUI software which is shown in

Fig. 6.1, designed by using MATLAB.

First we created a database of 10 fingerprints. The steps involved in it are as follows:

1. Image to the software with the details of the person:

Figure 6.1: Graphical User Interface(GUI) for creating database

After saving the personal information, we have to extract the features of the fin-

gerprint.

2. Applying Binarization technique:

Figure 6.2: Binarization

3. Applying Thinning process:

Figure 6.3: Thinning

36

4. Marking minutiae Points:

Figure 6.4: Minutiae extraction

5. Calculating and marking orientation field:

Figure 6.5: Orientation field

37

6. Marking the region of interest:

Figure 6.6: Marked region of interest

The above figure shows the output image of different operation performed on

it. The output numerical value obtain in background is used to match with the

fingerprint.

7. After creating the database, we match the fingerprints from it. The software takes

a latent image as input and matches the minutiae points and orientation with the

database and generates matching score. The following results were obtained:

Table 6.1: Results after comparing similarities between input with other fingerprintsin database FVC2002

FVC Database Input Match Score

101 1 101 1 1101 2 101 1 0.770102 1 101 1 0.197102 2 101 1 0.245103 1 101 1 0.180103 2 101 1 0.217104 1 101 1 0.247104 2 101 1 0.223

38

Table 6.2: Extracted information from image in term of ridge termination ,bifurcationand orientation field

Ridge termination Ridge bifurcation Orientation field144 60 135, 67150 66 133, 101172 97 198, 101127 109 192, 107146 120 136, 122212 127 220, 172191 131 0, 0168 136 0, 0153 145 0, 0132 152 0, 0115 157 0, 0211 157 0, 0215 162 0, 0133 192 0, 0114 197 0, 0180 202 0, 0139 208 0, 0211 214 0, 0192 215 0, 0145 218 0, 0167 225 0, 0163 232 0, 0168 239 0, 0190 241 0, 0215 243 0, 0194 246 0, 0158 249 0, 0

39

Figure 6.7: Matching with similar fingerprint

Figure 6.8: Matching with non-similar fingerprint

40

CHAPTER 7

CONCLUSION

The primary focus of the work in this project is on the enhancement of fingerprint im-

ages, and the subsequent extraction of minutiae. Firstly, we have implemented a series

of techniques for fingerprint image enhancement to facilitate the extraction of minutiae.

Experiments were then conducted using a combination of both synthetic test images

and real fingerprint images in order to provide a well-balanced evaluation on the perfor-

mance of the implemented algorithm. The use of synthetic images has provided a more

quantitative and accurate measure of the performance. Whereas real images rely on

qualitative measures of inspection, but can provide a more realistic evaluation as they

provide a natural representation of fingerprint imperfections such as noise and corrupted

elements. The experimental results have shown that combined with an accurate esti-

mation of the orientation and ridge frequency, our Automated Fingerprint Identification

System is able to effectively enhance the clarity of the ridge structures while reducing

noise. In contrast, for low quality images that exhibit high intensities of noise, the filter

is less effective in enhancing the image due to inaccurate estimation of the orientation

and ridge frequency parameters. However, in practice, this does not pose a significant

limitation as fingerprint matching techniques generally place more emphasis on the well-

defined regions, and will disregard an image if it is severely corrupted. Overall, the

results have shown that our Automated Fingerprint Identification System is useful to

employ prior to minutiae extraction.

CHAPTER 8

APPENDIX

8.1 Matlab Code

% ————————————————————————————————–

clear all;

clc;

addpath(genpath(pwd));

% % LOAD FINGERPRINT TEMPLATE DATABASE

load(’db.mat’)

% % EXTRACT FEATURES FROM AN ARBITRARY FINGERPRINT

[filename, PathName] = uigetfile(’ ∗ .jpg; ∗ .png; ∗ .tif; ∗ .jpeg; ∗ .bmp’,’Load image

File’);

img = imread( [PathName′/′filename] );

figure(1)

imshow(img)

img=imresize(img, [300300] );

if ndims(img) == 3; img = rgb2gray(img); end % Color Images

disp( [′Extractingfeaturesfrom′filename′...′] );

img=imadjust(img, [.3.7] , [] );

J=img(:,:,1) > 160;

figure(2)

imshow(J)

set(gcf,’position’, [11600600] );

K=bwmorph( ∼ J,’thin’,’inf’);

figure(3)

imshow(K)

ffnew=extMinutia(img,K)

figure(8)

% % CALCULATE MATCHING SCORE IN COMPARISION WITH FIRST ONE

load(’xdb.mat’)

for i=1:x

S(i)=match(ffnew,ffi);

drawnow

end

% % OFFER MATCHED FINGERPRINTS

Matched FigerPrints=find(S > 0.65)

% ————————————————————————————————–

% MINUTIAE EXTRACTION function [a5] = extMinutia(I,K)

fun=@minutie;

L = nlfilter(K, [33] ,fun);

LTerm=(L==1);

LTermLab=bwlabel(LTerm);

propTerm=regionprops(LTermLab,’Centroid’)

CentroidTerm=round(cat(1,propTerm(:).Centroid));

figure(4)

imshow(K)

hold on

plot(CentroidTerm(:,1),CentroidTerm(:,2),’ro’)

43

hold off

CentroidFinX=CentroidTerm(:,1);

CentroidFinY=CentroidTerm(:,2);

LSep=(L==3);

LSepLab=bwlabel(LSep);

propSep=regionprops(LSepLab,’Centroid’,’Image’);

CentroidSep=round(cat(1,propSep(:).Centroid));

CentroidSepX=CentroidSep(:,1);

CentroidSepY=CentroidSep(:,2);

figure(5)

imshow(K)

hold on

plot(CentroidSepX,CentroidSepY,’g ∗ ’)

hold off

figure(6)

imshow(K)

hold on

plot(CentroidTerm(:,1),CentroidTerm(:,2),’ro’)

plot(CentroidSepX,CentroidSepY,’g ∗ ’)

hold off

D=10;

% % Process 1

Distance=DistEuclidian( [CentroidSepXCentroidSepY ] , [CentroidF inXCentroidF inY ]

);

SpuriousMinutae=Distance < D;

[i, j] =find(SpuriousMinutae);

CentroidSepX(i)= [] ;

44

CentroidSepY(i)= [] ;

CentroidFinX(j)= [] ;

CentroidFinY(j)= [] ;

% % Process 2

D=7;

Distance=DistEuclidian( [CentroidSepXCentroidSepY ] );

SpuriousMinutae=Distance < D;

[i, j] =find(SpuriousMinutae);

CentroidSepX(i)= [] ;

CentroidSepY(i)= [] ;

D=6;

% % Process 3

Distance=DistEuclidian( [CentroidF inXCentroidF inY ] );

SpuriousMinutae=Distance < D;

[i, j] =find(SpuriousMinutae);

CentroidFinX(i)= [] ;

CentroidFinY(i)= [] ;

Kopen=imclose(K,strel(’square’,7));

KopenClean= imfill(Kopen,’holes’);

KopenClean=bwareaopen(KopenClean,5);

KopenClean( [1end] ,:)=0;

KopenClean(:, [1end] )=0;

ROI=imerode(KopenClean,strel(’disk’,10));

% % Suppress extrema minutiae

[m,n] =size(K(:,:,1));

indFin=sub2ind( [m,n] ,CentroidFinX,CentroidFinY);

Z=zeros(m,n);

45

Z(indFin)=1;

size(ROI’)

size(Z)

ZFin=Z. ∗ ROI’;

[CentroidF inX,CentroidF inY ] =find(ZFin);

indSep=sub2ind( [m,n] ,CentroidSepX,CentroidSepY);

Z=zeros(m,n);

Z(indSep)=1;

ZSep=Z. ∗ ROI’;

[CentroidSepX,CentroidSepY ] =find(ZSep);

figure(7)

imshow(I)

hold on

image(255 ∗ ROI)

alpha(0.5)

plot(CentroidFinX,CentroidFinY,’ro’,’linewidth’,2)

plot(CentroidSepX,CentroidSepY,’go’,’linewidth’,2)

hold off

m1=max(length(CentroidFinX),length(CentroidFinY));

m2=max(length(CentroidSepX),length(CentroidSepY));

m3=max(m1,m2)

a1 = [CentroidF inX(1 : length(CentroidF inX), 1); zeros([m3− length(CentroidF inX), 1])]

;

a2 = [CentroidF inY (1 : length(CentroidF inY ), 1); zeros([m3− length(CentroidF inY ), 1])]

;

a3 = [CentroidSepX(1 : length(CentroidSepX), 1); zeros([m3− length(CentroidSepX), 1])]

;

46

a4 = [CentroidSepY (1 : length(CentroidSepY ), 1); zeros([m3− length(CentroidSepY ), 1])]

;

a5= [a1, a2, a3, a4] ;

% ————————————————————————————————–

% COORDINATION TRANSFORM FUNCTION

function [T ] = transform( M, i )

Count=size(M,1);

XRef=M(i,1);

YRef=M(i,2);

ThRef=M(i,4);

T=zeros(Count,4);

R= [cos(ThRef)sin(ThRef)0; − sin(ThRef)cos(ThRef)0; 001] ; % Transformation Ma-

trix

for i=1:Count

B= [M(i, 1)−XRef ;M(i, 2)− Y Ref ;M(i, 4)− ThRef ] ;

T(i,1:3)=R ∗ B;

T(i,4)=M(i,3);

end

end

% ————————————————————————————————–

% COORDINATION TRANSFORM FUNCTION

function [Tnew] = transform2( T, alpha )

Count=size(T,1);

Tnew=zeros(Count,4);

R= [cos(alpha)sin(alpha)00; ...− sin(alpha)cos(alpha)00; ...0010; 0001] ; % Transfor-

mation Matrix

for i=1:Count

47

B=T(i,:)- [00alpha0] ;

Tnew(i,:)=R ∗ B’;

end

end

% ————————————————————————————————–

% RIDGEORIENTATION CALCULATION

function [orientim, reliability, coherence] = ... ridgeorient(im, gradientsigma, block-

sigma, orientsmoothsigma)

if ∼ exist(’orientsmoothsigma’, ’var’), orientsmoothsigma = 0;

end

[rows, cols] = size(im);

% Calculate image gradients.

sze = fix(6 ∗ gradientsigma); if ∼ mod(sze,2); sze = sze+1; end

f = fspecial(’gaussian’, sze, gradientsigma); % Generate Gaussian filter.

[fx, fy] = gradient(f); % Gradient of Gausian.

Gx = filter2(fx, im);

% Gradient of the image in x

Gy = filter2(fy, im);

% ... and y

% Estimate the local ridge orientation at each point by finding the Gxx = Gx. ∧ 2; %

Covariance data for the image gradients

Gxy = Gx. ∗ Gy;

Gyy = Gy. ∧ 2;

sze = fix(6 ∗ blocksigma);

if ∼ mod(sze,2);

sze = sze+1;

48

end

f = fspecial(’gaussian’, sze, blocksigma);

Gxx = filter2(f, Gxx);

Gxy = 2 ∗ filter2(f, Gxy);

Gyy = filter2(f, Gyy);

% Analytic solution of principal direction

denom = sqrt(Gxy. ∧ 2 + (Gxx - Gyy). ∧ 2) + eps; sin2theta = Gxy./denom; % Sine

and cosine of doubled angles

cos2theta = (Gxx-Gyy)./denom;

if orientsmoothsigma

sze = fix(6 ∗ orientsmoothsigma);

if ∼ mod(sze,2);

sze = sze+1;

end

f = fspecial(’gaussian’, sze, orientsmoothsigma);

cos2theta = filter2(f, cos2theta);

of sin2theta = filter2(f, sin2theta);

end

orientim = pi/2 + atan2(sin2theta,cos2theta)/2;

Imin = (Gyy+Gxx)/2 - (Gxx-Gyy). ∗ cos2theta/2 - Gxy. ∗ sin2theta/2;

Imax = Gyy+Gxx - Imin;

reliability = 1 - Imin./(Imax+.001);

coherence = ((Imax-Imin)./(Imax+Imin)). ∧ 2;

reliability = reliability. ∗ (denom > .001);

% ————————————————————————————————–

% PLOTING OF RIDGEORIENTATION function plotridgeorient(orient, spacing, im,

figno, I)

49

if fix(spacing) ∼ = spacing

error(’spacing must be an integer’);

end

[rows, cols] = size(orient);

lw = 2; % linewidth

len = 0.8*spacing; % length of orientation lines

% Subsample the orientation data according to the specified spacing

s orient = orient(spacing:spacing:rows-spacing, ...

spacing:spacing:cols-spacing);

xoff = len/2*cos(s orient);

yoff = len/2*sin(s orient);

if nargin > = 3 % Display fingerprint image

if nargin == 4

imshow(im, figno);

else

imshow(im);

end

end

% Determine placement of orientation vectors

[x, y] = meshgrid(spacing:spacing:cols-spacing, ...

spacing:spacing:rows-spacing);

x = x-xoff;

y = y-yoff;

% Orientation vectors

u = xoff*2;

v = yoff*2;

imshow(I)

50

hold on

quiver(x,y,u,v,0,’.’,’linewidth’,1, ’color’,’r’);

axis equal, axis ij, hold off

% ————————————————————————————————–

% RIDGE SEGMENTATION function [normim,mask,maskind] = ridgesegment(im,

blksze, thresh)

im = normalise(im); % normalise to have zero mean, unit std dev

fun = inline(’std(x(:))*ones(size(x))’);

stddevim = blkproc(im, [blksze blksze], fun);

mask = stddevim > thresh; maskind = find(mask);

% Renormalise image so that the *ridge regions* have zero mean, unit % standard

deviation. im = im - mean(im(maskind)); normim = im/std(im(maskind)); % ———

—————————————————————————————–

% COORDINATION TRANSFORM FUNCTION

function [normim,mask,maskind] = ridgesegment(im, blksze, thresh)

im = normalise(im);

fun = inline(’std(x(:)) ∗ ones(size(x))’);

stddevim = blkproc(im, [blkszeblksze] , fun);

mask = stddevim > thresh;

maskind = find(mask);

im = im - mean(im(maskind));

normim = im/std(im(maskind));

% ————————————————————————————————–

function n = normalise(im, reqmean, reqvar)

if ∼ (nargin == 1 — nargin == 3)

error(’No of arguments must be 1 or 3’);

end

51

if nargin == 1 % Normalise 0 1

if ndims(im) == 3

hsv = rgb2hsv(im);

v = hsv(:,:,3);

v = v - min(v(:));

v = v/max(v(:));

hsv(:,:,3) = v;

n = hsv2rgb(hsv);

else % Assume greyscale

if ∼ isa(im,’double’), im = double(im);

end n = im - min(im(:));

n = n/max(n(:));

end

else % Normalise to desired mean and variance

if ndims(im) == 3 % colour image

error(’cannot normalise colour image to desired mean and variance’);

end

if ∼ isa(im,’double’), im = double(im); end im = im - mean(im(:));

im = im/std(im(:));

n = reqmean + im ∗ sqrt(reqvar);

end

% ————————————————————————————————–

% TRANSFORMED MINUTIAE MATCHING SCORE

function [sm] = score( T1, T2 )

Count1=size(T1,1);

Count2=size(T2,1);

n=0; T=15;

52

TT=14;

for i=1:Count1

Found=0; j=1;

while (Found==0) && (j < =Count2)

dx=(T1(i,1)-T2(j,1));

dy=(T1(i,2)-T2(j,2));

d=sqrt(dx ∧ 2+dy ∧ 2);

if d < T

DTheta=abs(T1(i,3)-T2(j,3)) ∗ 180/pi;

DTheta=min(DTheta,360-DTheta);

if DTheta < TT

n=n+1;

Found=1;

end

end

j=j+1;

end

end

sm=sqrt(n ∧ 2/(Count1 ∗ Count2)); % Similarity Index

end

% ————————————————————————————————–

function D=DistEuclidian(dataset1,dataset2)

h = waitbar(0,’Distance Computation’);

switch nargin

case 1

[m1, n1] =size(dataset1);

m2=m1;

53

D=zeros(m1,m2);

for i=1:m1

waitbar(i/m1)

for j=1:m2

if i==j

D(i,j)=NaN;

else D(i,j)=sqrt((dataset1(i,1)-dataset1(j,1)) ∧ 2+(dataset1(i,2)-dataset1(j,2)) ∧ 2);

end

end

end

case 2

[m1, n1] =size(dataset1);

[m2, n2] =size(dataset2);

D=zeros(m1,m2);

for i=1:m1

waitbar(i/m1)

for j=1:m2

D(i,j)=sqrt((dataset1(i,1)-dataset2(j,1)) ∧ 2+(dataset1(i,2)-dataset2(j,2)) ∧ 2);

end

end

otherwise error(’only one or two input arguments’)

end

close(h)

% ————————————————————————————————–

% FINGERPRINT MATCHING SCORE

function [S] = match( M1, M2, display flag )

if nargin==2;

54

display flag=0;

end

M1=M1(M1(:,3) < 5,:);

M2=M2(M2(:,3) < 5,:);

count1=size(M1,1);

count2=size(M2,1);

bi=0; bj=0; ba=0; % Best i,j,alpha

S=0;

% Best Similarity Score

for i=1:count1

T1=transform(M1,i);

for j=1:count2

if M1(i,3)==M2(j,3)

T2=transform(M2,j);

for a=-5:5

% Alpha

T3=transform2(T2,a ∗ pi/180);

sm=score(T1,T3);

if S < sm

S=sm;

bi=i;

bj=j;

ba=a;

end

end

end

end

55

end

if display flag==1

figure, title( [′SimilarityMeasure :′ num2str(S)] );

T1=transform(M1,bi);

T2=transform(M2,bj);

T3=transform2(T2,ba ∗ pi/180);

plot data(T1,1);

plot data(T3,2);

end

end

% ————————————————————————————————–

56

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